↑ Sample Size ⟶ ↑ Power for GWAS
But this strategy is generally not successful for interaction GWAS
FEV1/FEV1-FVC on 50,008 individuals from UK Biobank (Wain et al. 2015) reveals 0 SNP-smoking interactions (p < 5e-08)One of the alternative strategies is to aggregate genetic variants:
(Renier et al. 2017)
(Aschard et al. 2015)
FEV1/FEV1pp, FVC/FVCpp, FEV1_FVC, pctEmph_Slicer, TLCpp, finalGoldSmokCigNow, CigPerDayNow > 15, CompletedSchool > 237K long (>10kb) local ancestry segments (Parker et al. 2014)
The COPDGene dataset is appropriate, as the proportion of African ancestry is associated with the risk of COPD (Kumar et al. 2010)
Confounding factors other than global ancestry \(a_g\):
FEV1 ~ Age + Age^2 + Gender + Height + PackYears + SmokCigNowpctEmph_Slicer traitThis (marginal) model was used in (Parker et al. 2014).
Is it OK for interaction?
SmokCigNow + ATS_PackYears → Duration_Smoking + log_CigPerDaySmokAvg + SmokCigNow + SmokCigNow0_15 + SmokCigarNowMore details in our previous talk COPDGene African-Americans & QQ plots
SmokCigNow (7 traits)\(z = [z_1; z_2; \dots]^T \sim N(0, \Sigma)\)
under the null hypothesis
2.5Bonferroni 0.05 / 37K = 1.4e-06
| Trait | Exposure | z-score | p-value |
|---|---|---|---|
| FEV1pp | SmokCigNow | 4.5 | 7.3e-06 |
| Omnibus | SmokCigNow | – | 5.7e-05 |
| FEV1_FVC | SmokCigNow | 3.9 | 1.1e-04 |
| FEV1 | SmokCigNow | 3.8 | 1.4e-04 |
Ancestry segment: 2:238,819,792 - 238,904,351
Genes within \(\pm\) 100kb: TWIST2, HDAC4, MIR4440, MIR4441
| Trait | Exposure | z-score | p-value |
|---|---|---|---|
| FEV1 | SmokCigNow0_15 | 4.2 | 2.6e-05 |
| FEV1pp | SmokCigNow0_15 | 4.1 | 4.9e-05 |
| FVC | SmokCigNow0_15 | 4.0 | 6.9e-05 |
| Omnibus | SmokCigNow_15 | – | 1.5e-04 |
| FVCpp | SmokCigNow0_15 | 3.7 | 2.5e-04 |
| Data | Min size | Mean size | Genome coverage |
|---|---|---|---|
| Local ancestry | 10kb | 13kp | 74% |
| ENCODE annotation | – | 0.150kb | 10% |
| Intersection | 10kb | 11kb | 70% |
Leaveraging Methylation data
We observed top associated genes have been published to be associated with epigenetic changes.
Hypothesis: the mechanism of ancestry-based association is:
Smoking → Up/Down Methylation → COPD-related phenotype
Wan et al., Smoking-associated site-specific differential methylation in buccal mucosa in the COPDGene study (2015)
Aschard et al. 2015. “Leveraging local ancestry to detect gene-gene interactions in genome-wide data.” BMC Genetics 16 (1). BMC Genetics: 124. doi:10.1186/s12863-015-0283-z.
———. 2017. “Evidence for large-scale gene-by-smoking interaction effects on pulmonary function.” International Journal of Epidemiology 46 (3): 894–904. doi:10.1093/ije/dyw318.
Kumar et al. 2010. “Genetic Ancestry in Lung-Function Predictions.” New England Journal of Medicine 363 (4): 321–30. doi:10.1056/NEJMoa0907897.
Park et al. 2016. “An Ancestry Based Approach for Detecting Interactions.”
Parker et al. 2014. “Admixture mapping identifies a quantitative trait locus associated with FEV1/FVC in the COPDGene Study.” Genetic Epidemiology 38 (7): 652–59. doi:10.1002/gepi.21847.
Renier et al. 2017. “HHS Public Access” 165 (7): 1789–1802. doi:10.1016/j.cell.2016.05.007.Mapping.
Sul et al. 2016. “Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.” PLoS Genetics 12 (3): e1005849. doi:10.1371/journal.pgen.1005849.
Wain et al. 2015. “Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): A genetic association study in UK Biobank.” The Lancet Respiratory Medicine 3 (10): 769–81. doi:10.1016/S2213-2600(15)00283-0.
Zaitlen et al. 2014. “Leveraging population admixture to characterize the heritability of complex traits.” Nature Genetics 46 (12). Nature Publishing Group: 1356–62. doi:10.1038/ng.3139.